yconst

Our brains have this giant section dedicated to nothing but recognizing characters in our native written language. Sure we have a lot of brain power going to shape recognition in general, but character recognition is so incredibly trained in our heads that it should be our go to.

Yea, but you still have to read them, i.e. focus this "machinery" on a single word/sentence. So you're working in sequence when trying to identify a bunch of unrelated labels. On the contrary, you can probably differentiate an entire group of icons just by a glance in their direction (provided they are appropriately designed, of course).

Plus reading speed is pretty much the same no matter how many times you repeat the task. Recognition on the other hand improves with repetition.

If you're using only text, you're pretty much stuck with being able to differentiate your icons either by location or by actually reading them. Icons add another component to this.

Totally agree with the color-related comment though.

IANANS btw, so your mileage may vary.

Interesting point. However, I'd like to argue that there is a fine line between the conventional definition of a utility function and the ability of an organism to survive in an environment. The latter is truly open-ended, and not really externally imposed, i.e. different animals/people fare throughout life "optimising" completely different functions (closure?). A quote from a Kozma paper comes to mind: "Intelligence is characterized by the flexible and creative pursuit of endogenously defined goals". I believe this quite well summarises the open-ended nature of the task at hand. As I understand it, indeed, goals, rewards, risks, hazards, they are all in the game and shape the decision-making of the agent, it's "policy". But the way they are formalized for each individual and situation, well, is probably subject to constant redefinition itself.

I found the first comment to this article quite interesting:

"Marcus has a point - even is some of what is said about deep neural networks is incorrect (for instance, they can learn and generalize from very few example, one shot learning).

However, he got it wrong with the answer. The key for machines to reach the symbolic abstraction level is the way we train them. All training algorithms, supervised, unsupervised or reinforcement learning with LSTM rely on the assumption that there is an "utility function" imposed by some external entity. Problem is, by doing so, we are taking away their capacity [of the machines] to make questions and create meaning.

The most important algorithm for learning is "meaning maximization" not utility maximization. The hard part is that we cannot define what is meaning - maybe we can't, I'm not sure. That is something I will be glad to discuss."

Precisely the same question I have as well. Especially given that the energy you could store in a spring or coil could be much more, and you are not limited by one's lifting capacity or maximum height. Anyone have an idea?

"Do I dare to say this somehow shows the relative unimportance of design?"

When design is about cramming some animation in just about everything you can show on the screen, and about how fancy your colors are going to be – yes, I agree, it's relatively unimportant.

But real design is not about that at all. In fact, functionality has been part of the core debate in design, for at least a century now. Ask any designer – not only the ones dealing with UI, and they'll tell you.

"We could also take other data into account, like the user who submitted the article, and generate features indicating things like the karma of the user..."

I'm wondering how helpful could that be in prediction though? Would it actually help if I wish to predict how many upvotes my headline would get, and I add my karma as a feature? I think in fact such features would degrade generalisation performance, as they stand in like placeholders (when training), i.e. high karma users are correlated to higher probability for a "hit story".

It does not elaborate on the "problem of crowds", at all. On the contrary, it offers a rather positive view to the whole spontaneous reaction thing. Which is great, don't get me wrong. However, in this case, I believe the solution could have been much much simpler to begin with. But that's just me :)

So that's all great and trying to help and all, but.. a crowd that yells at the top of their lungs to a driver till he goes in tears (in place of actually guiding him to properly move the vehicle), and thereafter tries to lift the bus using nothing but brute force.. isn't this like creating a problem and then trying to solve it?

A related thought might be, once this is in place, how much work would it be so support Appkit (OS X) apps as well?

The fundamental issue is that glass is a blatantly obvious design, with no sophistication whatsoever, one that couldn't even get close to being charmingly anything. It's an ugly stick in front of some (admittedly ugly/geeky) glasses, with touchpad (!) and voice controls crammed together. No amount of functionality can account for the design and usability horror that this device has unleashed since it's conception.

But it will probably not scale linearly.

Seems $2k is just one of their milestones...

"development is split into milestones on which the features are discussed and decided upon in the mailing list with the help of the community.

a funding campaign is then setup for each next milestone which supports development and keeps the project up and alive."

Very interesting project. Would be great to get some more info on the actual algorithms being used. The about page offers relatively limited info.

Think of it this way: you could design your logos etc. using Swift, in interactive mode!

I wonder if this brings us one step closer to developing on iOS...

This is massive.

Some functionality for working with XIBs in an intuitive way would be a great feature. Same for Core Data models.

Your last argument, I find it irrelevant. Buttons and links etc. change colours not because of their inability to indicate their function, but to enhance the interactive experience. In most cases you get exactly the same effect with so-called "skeyomorphic" elements, no difference at all. The whole discussion has nothing to do with your "underlined links" argument.

The flat look has received a lot of criticism in the past couple years, but to be completely honest, in the many arguments I've heard against it, I couldn't really find one that can really stand it's ground if I may say so. On the contrary, I find a lot of merit in the new principles that are being embraced trough this approach, such as e.g. the emphasis on interaction to distinguish elements, instead of artificially imposed "symbolisms" (whether this is an underline, or a button-shaped, well, button :) ). This gives me a hint that, as a design community, we have a more mature approach to designing for those unearthly things we call "devices" than we did a few years back :)

I'd def go for ROTATE-SIDE-DOWN PERSPECTIVE. Clear, concise and intuitive.

Dear HN users: If you are even minimally interested in the topics that this post "covers", please, do yourselves a favour and open up any book about machine intelligence instead of reading such uninformed and negligent posts.

I urge you to, seriously.

Very good point! It's not the reasoning and logic behind regex that would turn a beginner away from it, its the syntax that is being used that often results into expressions turning into impenetrable goo!

(thanks for the verbal expressions link btw, seems really promising)

Looking this up on Wikipedia reveals it's relationship with the Three Prisoners Problem[1] as well. The solution presented in that case helped me in reasoning about it.

[1] http://en.wikipedia.org/wiki/Three_Prisoners_problem

A little bit more info about the internals of the recognition engine would be welcome. It seems that one needs to manually supply sets of keywords and key phrases?

"The shift from procedural to OO brings with it a shift from thinking about problems and solutions to thinking about architecture."

Interesting thinking, never thought of it this way.

Not even having read the article, I just want to express my disgust towards the often used "up to x" type statistics and their deceptive misguiding nature.

Icons look cool, but it seems to me that the constant presence of the hand creates two problems here: 1. It dramatically reduces the clarity of the signs (the dots and arrows), which is actually the part of the icon that DOES guide you (think as well of small sizes/low resolutions), and 2. (as ronaldx mentions already) it adds a repeating motif which I'm sure after a while might become uninteresting, and also may very much graphically define the app it is used in.

As a suggestion, perhaps the relative sizes between the hands and the signs could be reconsidered, and a more abstract level of representation of the hand could be thought of.

Looks quite cool and such. But what do you actually do in this game? I mean, in what ways do you interact with your environment?

Looks good! What performance gain could be expected when using GPU vs CPU for Deep Learning? It'd be great to get some figures.

The new bar is so imposing compared to the rest of the interface.